Sharpness metric for asymmetrically enhanced image and video

ABSTRACT

A sharpness metric represents a control variable of manual ( 47 ) or automated ( 41 ) sharpness control systems for image and video acquisition, storage and reproduction systems. In manual systems usually one controllable parameter is adjusted seeking to maximize sharpness, within pre-established limits to avoid image distortion. A method for measuring sharpness ( 10 ) in an image or picture that may have been enhanced asymmetrically uses statistics from a Discrete Cosine Transformation on predetermined blocks of the image and compensates for asymmetry using information on a number of edge pixels ( 14 ) and an energy content of one or more vertical edges and one or more horizontal edges in each block ( 15 ). One embodiment for so doing determines a kurtosisbased sharpness metric of the image ( 12 ) and then compensates the kurtosis-based sharpness metric to account for differences in sharpness enhancement in a horizontal direction and a vertical direction ( 13 ).

The present invention relates generally methods and apparatuses forprocessing video and image data, and more particularly to a method andapparatus for encoding and decoding video and image data foracquisition, transmission and storage systems.

Measuring sharpness of a video image implies assessing the definition ofthe edges and the clarity of the details with respect to the background.When an image or video is enhanced asymmetrically, i.e., the amount ofhorizontal enhancement is different from the vertical enhancement,values given by existing metrics do not correspond to the perceivedresults in visual tests. For example, some existing techniques comparesharpness of images as long as the relative proportion of horizontalsharpness and vertical sharpness is not modified. When this proportionis changed, the end result is similar to comparing different images,thus making these metrics ineffective in providing consistent results.

A sharpness metric is used in many image capture and display systems toautomate sharpness control, enable customizable sharpness settings, andto provide adaptive sharpness enhancement. A sharpness metric can alsobe used as a control variable for sharpness enhancement algorithms inhigh-quality digital video, or as a quality indicator for situations inwhich quality is sufficiently high and other factors remain constant.Combined with other metrics, sharpness can be used to compute overallquality.

Asymmetric sharpness enhancement is an important option used byalgorithms that adapt the extent of enhancement to the actual content.Asymmetric sharpness enhancement may arise from the use of a low costhardware implementation option of 2D sharpness enhancement that uses 1Dfilters (often found in present day TV sets). The flexibility of theapplication of 1D filters, and content adaptive enhancement techniques,may result in asymmetric sharpness enhancement. Presently, there is nomethod for measure sharpness under these conditions.

The present invention is therefore directed to the problem of developinga method and apparatus for quantifying the sharpness of a video image orpicture that will operate adequately when an image or picture has beenasymmetrically enhanced.

The present invention solves these and other problems by providing amethod for measuring asymmetric sharpness enhancement, which usesstatistics of a Discrete Cosine Transformation (DCT) taken oneight-by-eight (8×8) blocks (or another convenient size forimplementation, in this case 8×8 is compatible with existingimplementations of block DCT algorithms) and compensates for asymmetryusing information on the number of edge pixels and the energy ofvertical and horizontal edges.

According to one aspect of the present invention, a method for measuringsharpness in an image or picture that has been partitioned into one ormore blocks employs a kurtosis-based sharpness metric on the image andcompensates the kurtosis-based sharpness metric to account fordifferences in sharpness enhancement in a horizontal direction and avertical direction.

According to another aspect of the present invention, the compensationincludes adding a term to the kurtosis-based sharpness metric based onan average number of edge pixels per block ({overscore (nep)}),estimated over the entire image or a sample of it.

According to yet another aspect of the present invention, thecompensation includes adding a term to the kurtosis-based sharpnessmetric based on an average horizontal energy ({overscore (E_(x))}) andan average vertical energy ({overscore (E_(y))}), either estimated overthe entire image or from a sample of the image.

According to still another aspect of the present invention, thecompensation includes adding a term to the kurtosis-based sharpnessmetric based on an average horizontal energy ({overscore (E_(x))}) andan average vertical energy ({overscore (E_(y))}) and an average diagonalenergy ({overscore (E_(d))}), either estimated over the entire image orfrom a sample of the image.

According to yet another aspect of the present invention, thecompensation includes adding a term to the kurtosis-based sharpnessmetric based on a number of blocks that contain edges (neb) and a numberof blocks that do not contain edges (nfb). In this case, actual valuesfrom the entire image or estimates can be used.

FIG. 1 depicts an exemplary embodiment of a method for measuringsharpness in an asymmetrically enhanced image or picture according toone aspect of the present invention.

FIG. 2 depicts an exemplary embodiment of a method for computing variousenergies in an 8×8 Discrete Cosine Transform according to another aspectof the present invention.

FIG. 3 depicts a plot of an average 8×8 Discrete Cosine Transform foredge blocks showing the effect of sharpness enhancement.

FIG. 4 depicts a generic architecture illustrating different embodimentsincluding manual sharpness control and automated sharpness control forimage/video acquisition, storage, and reproduction systems.

It is worthy to note that any reference herein to “one embodiment” or“an embodiment” means that a particular feature, structure, orcharacteristic described in connection with the embodiment is includedin at least one embodiment of the invention. The appearances of thephrase “in one embodiment” in various places in the specification arenot necessarily all referring to the same embodiment.

Image post processing and enhancement has become a critical componentfor digital television systems particularly for high resolution and highdefinition technologies (comprised image acquisition, storage andreproduction systems). Professional applications such as medicalimaging, radar imaging, optical imaging, etc. can also use embodimentsof this invention. To assess the effectiveness and control the amount ofenhancement, the only solution is to use quality metrics, specificallysharpness metrics. Sharpness is the informal, subjective perception ofthe clarity of detail and the edges seen in an image. Research on imageanalysis and perception has shown that sharpness is highly dependent oncontent, and also on spatial resolution, contrast, and noise.

State of the art enhancement algorithms use asymmetric enhancement inorder to increase perceived quality. For example, in many casesenhancing vertical edges has more perceptual impact than enhancinghorizontal edges by the same amount. Existing sharpness metrics cannotdeal with this case. The present invention allows monitoring andcontrolling those sharpness enhancement algorithms and other processingthat results in asymmetric changes in sharpness.

Embodiments of the present invention may be implemented in sharpnessenhancement modules for televisions (e.g., STD, BDTV, LCDTC, PDP,LCOSTV), automatic television control, as well as storage and playbackequipment (DVD, DVD-RW, etc.). The sharpness metric is also a componentof overall quality metrics for use in the same products and othersrelated to video quality of service. An embodiment of an apparatus foremploying the metric calculation of the present invention is shown inFIG. 4.

The 1-dimensional (1D) and 2-dimensional (2D) kurtosis of the frequencyspectrum (FFT and DCT) can be useful when determining sharpness metrics.Moreover, sharpness can be measured without the use of a fixed originalas reference. The sharpness metric based on the local edge kurtosis hasalso been incorporated into a no-reference, overall quality metric.

When applying the sharpness metric to the control of sharpnessenhancement algorithms, the kurtosis-based metric does not perform wellwhen asymmetric sharpness enhancement, i.e., different horizontal andvertical gain, is used. Unfortunately, asymmetric enhancement isfrequently used in order to adapt to content as well as to thesensitivity of the human visual system.

Consensus observations by local researchers, also confirmed bysubjective testing, indicate that using a 2d kernel results in sharpnessthat is larger or comparable to any 1d kernel, and that the relativeeffect of 1dh and 1dv enhancement depends on content. However, usingonly the kurtosis metric, in most cases the 2D kernel using equalamounts of vertical and horizontal sharpness ranks near the bottom andoccasionally in the middle. The results are similar for interlaced andprogressive video.

Therefore, a kurtosis sharpness metric did not accurately reflect theperceived effect of the 2D enhancement in any of the cases tested.

The increase in sharpness measured for increasing gains of the samekernel appears to be well behaved for the kurtosis metric, thusindicating that the metric may not be taking into account some factorthat makes the curve for 2D enhancement less steep than the 1D cases.Therefore, other image parameters must be used to compensate for the lowsensitivity to 2D enhancement.

Kurtosis is a measure of the “peakedness” of a distribution. A normaldistribution has a kurtosis value of three (3), which increases if thepeak is higher and the curve narrower. In the case of an 8×8 DiscreteCosine Transformation (DCT), the surface is not normal, or symmetric,but it can be considered as one quadrant of a symmetric surface wherepeakedness can be partially recognized. Changes in the DCT surfacecaused by symmetric (2D) sharpness enhancement are reflected by anincrease in kurtosis.

FIG. 3 shows the surface plots for the average 8×8 DCT taken over allblocks that contain edges for an original image, a 1DH enhanced versionof the same image, a 1DV enhanced version of the same image, and a 2Denhanced version of the same image. The effect of sharpness enhancementproduces shifts of the surface towards the higher frequencies, and aswelling effect on the same surface that affects the frequenciesaffected by the kernel (shown by black arrows in FIG. 3). Those effectspush kurtosis values up as if the center of gravity is moving upwards.

For a certain test image, a 1D enhancement in the vertical direction hasa much stronger effect on the 2D kurtosis than an enhancement in thehorizontal direction. A 1D enhancement in the vertical direction causesa much larger shift of kurtosis than a 2D enhancement that uses the samegain. Notice the more moderate and symmetric effect of the 2Denhancement (2D1 kernel) on the DCT on the surface profile and peaks ascompared to the effect of the 1D enhancements in FIG. 3.

The high sensitivity of 2D kurtosis of the DCT to asymmetric processing,suggests that other factors should be taken into account to compensatefor asymmetry while preserving the ability to reflect changes in edgesharpness. Two potential compensation factors are considered: edgeextent and edge energy in the two directions.

In order to find a more complete model that accounts for sharpness underasymmetric enhancement we use a methodology often used in mathematicalmodeling, which consists of analyzing the graphs for one variable (orequivalent) at a time and make inferences as to its influence on themodel.

We analyze the influence of edge extent by looking at the average numberof edge pixels inside 8×8 blocks that contain edges. For the imagesstudied, we notice that the number of edges follows well the observedincrease in sharpness. We have determined that 2D enhancement causes thelargest increase in average number of edge pixels, especially largerthan that of the 1DV enhancement.

Thus, the average number of edge pixels appears to be relevant for thecompensation of kurtosis-based sharpness; it reflects perceiveddifferences across enhancement methods. However, edge extent worksmainly for enhancement algorithms that use the peaking method; othermethods may not cause an increase in the number of edge pixels. We havefound that sharpness enhancement resulting from enhanced resolution,used in scalable coders or format conversion, does not cause, and it isnot expected to cause an increase in the number of edge pixels.Therefore, another compensation factor is necessary besides the edgeextent.

Next, we looked at the amount of vertical and horizontal edge energycontained in the 8×8 DCT for blocks that contain edges. FIG. 2 shows themethod used to calculate horizontal, vertical, and diagonal energy of an8×8 DCT.

Graphing the ratio between average horizontal energy and diagonal energy(Ex/Ey) for a subset of test images shows relative ranking closer tothat of the subjective observations for the 1DH, 1DV, and 2D1 enhancedsequences. The results show higher rankings for 2D and 1DV, while the1DH curve is consistently below the others.

Further analysis of horizontal, vertical and diagonal energy suggeststhat terms such as the ratio of the geometric mean to the arithmeticmean of horizontal and vertical energy can also be used to compensatefor the asymmetry that leads to the exaggerated sharpness valuesobtained using the kurtosis-based metric.

In principle, modulating the kurtosis by functions of the edge extentand energy can preserve the inter-kernel rankings and shift the curvesto capture the correct intra-kernel rankings. The next sections show howthis may be accomplished.

In order to propose a function that compensates the kurtosis-basedsharpness metric, we have analyzed the behavior of four terms associatedwith the following global image features:

-   1. Average number or edge pixels per block ({overscore (nep)}). As    explained before, for methods that increase edge extent, this value    gives the expected rankings across kernels.-   2. Ratio of the sum of average horizontal, average vertical, and    average diagonal energies to the average diagonal energy (i.e.,    $\left( {{i.e.},\frac{{\overset{\_}{E}}_{x} + {\overset{\_}{E}}_{y} + {\overset{\_}{E}}_{d}}{{\overset{\_}{E}}_{d}}} \right)$    for blocks that contain edges. This term is the total energy    normalized by the average diagonal energy, which also includes the    contribution of textures (textures are not so important as edges to    assess sharpness).-   3. Ratio of geometric to arithmetic mean of average horizontal and    average vertical energies    $\frac{4*{\overset{\_}{E}}_{x}*{\overset{\_}{E}}_{y}}{\left( {{\overset{\_}{E}}_{x} + {\overset{\_}{E}}_{y}} \right)^{2}}$    raised to the power of 2. This ratio is an eccentricity (Exc) or    asymmetry factor, which has a maximum of 1 for symmetric spectra.    Its value decreases as the asymmetry goes up.-   4. Ratio of the number of blocks that do contain edges to the number    of blocks that do not contain edges, or flat blocks, (neb/nfb). This    is an important perceptual factor as the perceived sharpness is    higher if there are more edge blocks.

We propose a mathematical formula that consists of the combination ofthe average kurtosis for edge blocks ({overscore (_(k))}) and the termsabove. Many combinations are possible; we have tested several and cameup with a general formulation, which shows the desired modulation of theaverage kurtosis by the energy and edge extent terms: $\begin{matrix}{{Sh} = {{f_{1}\left\lbrack {C_{1} + {C_{2}*\overset{\_}{k}*\overset{\_}{nep}*\frac{\left( {{\overset{\_}{E}}_{x} + {\overset{\_}{E}}_{y} + {\overset{\_}{E}}_{d}} \right)}{{\overset{\_}{E}}_{d}}*\frac{4*{\overset{\_}{E}}_{x}*{\overset{\_}{E}}_{y}}{\left( {{\overset{\_}{E}}_{x} + {\overset{\_}{E}}_{y}} \right)^{2}}*\frac{neb}{nfb}}} \right\rbrack} + {C_{3}*{\overset{\_}{nep}.}}}} & (1)\end{matrix}$f₁ is a logarithmic function ln(x), and constants C₁, C₂, and C₃ aredetermined experimentally, we use values C₁=1, C₂=0.1, and C₃=0.1, whichare believe near the optimum but may be further tuned up based on futureexperimental data.

Turning to FIG. 1, shown therein is an exemplary embodiment 10 of amethod for measuring sharpness in an image or picture. After the imageor picture is partitioned into one or more blocks (e.g., 8×8 or someother convenient size (element 11), a kurtosis-based sharpness metric ofthe image is determined (element 12). This metric is then compensated toaccount for differences in sharpness enhancement in a horizontaldirection and a vertical direction (element 13). One compensationtechnique compensates by adding a term to the kurtosis-based sharpnessmetric based on an average number of edge pixels per block (element 14).Compensation can also occur by adding a term to the kurtosis-basedsharpness metric based on an average horizontal energy and an averagevertical energy and an average diagonal energy (these energies can becalculated over the entire image or estimated from a sample of theimage) (element 15). Moreover, a term can be added to the kurtosis-basedsharpness metric based on a geometric mean of the average horizontalenergy and the average vertical energy and an arithmetic mean of theaverage horizontal energy and the average vertical energy (element 16).Furthermore, a term can be added to the kurtosis-based sharpness metricbased on a number of blocks that contain edges (neb) and a number ofblocks that do not contain edges (nfb) (element 17). The abovecalculations are summarized in the following equation: $\begin{matrix}{{Sh} = {{f_{1}\left\lbrack {C_{1} + {C_{2}*\overset{\_}{k}*\overset{\_}{nep}*\frac{\left( {{\overset{\_}{E}}_{x} + {\overset{\_}{E}}_{y} + {\overset{\_}{E}}_{d}} \right)}{{\overset{\_}{E}}_{d}}*\frac{4*{\overset{\_}{E}}_{x}*{\overset{\_}{E}}_{y}}{\left( {{\overset{\_}{E}}_{x} + {\overset{\_}{E}}_{y}} \right)^{2}}*\frac{neb}{nfb}}} \right\rbrack} + {C_{3}*{\overset{\_}{nep}.}}}} & \left( {{element}\quad 18} \right)\end{matrix}$

The above sharpness metric, which incorporates edge and energycompensation, has been tested on several images. The results indicatethat the 2D kernels exhibit higher sharpness than the 1D kernels. Testresults indicate that the 2D kernels are consistently better than the 1Dkernels.

Previous results, in which the kurtosis-based sharpness metric showedproper intra-kernel behavior, have been preserved. An interesting caseis that of resolution enhanced video, which shows different levels ofsharpness corresponding to the levels of perceived quality. Thecompensated sharpness metric values, plotted frame-by-frame show thatsharpness levels correspond with the visual observations, i.e., highersharpness for higher resolution. Either averaging over a time window orusing values per frame, the sharpness metric is effective to detectchanges due to enhancement.

Testing indicates that the performance of the prior kurtosis metric hasbeen preserved while improving results for asymmetric sharpnessenhancements. Kurtosis of the edge regions is a very promising indicatorof sharpness, and if compensated for edge extent and energy asymmetrysuch kurtosis can also deal with asymmetric sharpness enhancement. Thecompensation terms used in this work are all global, that is, averagevalues taken over the blocks that contain edges. Local kurtosis can alsobe compensated to measure sharpness at the local level using aprobabilistic approach derived from the global statistics. The termsused so far reflect the global statistical aspects of the image whilethe specific conditions at the local level can deviate largely from theaverage, for example the number of edge pixels in a block varies from 1to 28 or more, and the energy values can also change broadly. Thus, topredict local enhancement we can use models derived from global data.

FIG. 4 depicts a block diagram of a general embodiment 40 showing eithera manual sharpness controller 47 or an automatic sharpness controller 41used in, for example, acquisition, storage and reproduction video/imagesystems. In an automatic sharpness controller 41, the sharpness metricis computed from the image or part of it, and controllable parameters inthe video chain modules 42-45 are acted upon in order to maximizesharpness within allowable range. The image source can be an acquisitionmodule (e.g., CCD in a camcorder 48 d, optical imagers 48 a-c, or astorage unit 48 e, such as a VCR, DVD, CD or HD. To detect if someone isusing a system that uses symmetric sharpness control, one can simplyinput an asymmetrically enhanced image, and it would not enhance itanymore, e.g., the system would treat the image as already at a maximumsharpness, when it has been vertically enhanced. A symmetry compensatedsystem would enhance the image in both directions as much as possible.Test patterns made of horizontal and vertical edges would be very easyto use for this purpose.

Although various embodiments are specifically illustrated and describedherein, it will be appreciated that modifications and variations of theinvention are covered by the above teachings and are within the purviewof the appended claims without departing from the spirit and intendedscope of the invention. For example, certain forms of equations are usedto model sharpness, however, other functions employing similarcompensation terms can be used without departing from the scope of thepresent invention. Furthermore, this example should not be interpretedto limit the modifications and variations of the invention covered bythe claims but is merely illustrative of one possible variation.

1. A method for measuring sharpness in an image or picture comprising:partitioning the image or picture into one or more blocks, each of whichhas a predetermined size and repeating the following for each of the oneor more blocks (11): determining a kurtosis-based sharpness metric ofthe image (12); and compensating the kurtosis-based sharpness metric toaccount for differences in sharpness enhancement in a horizontaldirection and a vertical direction (13).
 2. The method according toclaim 1, wherein said compensating includes adding a term to thekurtosis-based sharpness metric based on an average number of edgepixels per block ({overscore (nep)}) (14).
 3. The method according toclaim 1, wherein said compensating includes adding a term to thekurtosis-based sharpness metric based on an average horizontal energy({overscore (E_(x))}) and an average vertical energy ({overscore(E_(y))}) (15).
 4. The method according to claim 1, wherein saidcompensating includes adding a term to the kurtosis-based sharpnessmetric based on an average horizontal energy ({overscore (E_(x))}) andan average vertical energy ({overscore (E_(y))}) and an average diagonalenergy ({overscore (E_(d))}) (15).
 5. The method according to claim 1,wherein said compensating includes adding a term to the kurtosis-basedsharpness metric based on a geometric mean (E_(x)*E_(y))^(1/2) of theaverage horizontal energy ({overscore (E_(x))}) and the average verticalenergy ({overscore (E_(y))}) (16).
 6. The method according to claim 1,wherein said compensating includes adding a term to the kurtosis-basedsharpness metric based on an arithmetic mean [½({overscore(E_(x))}+{overscore (E_(y))})] of the average horizontal energy({overscore (E_(x))}) and the average vertical energy ({overscore(E_(y))}) (16).
 7. The method according to claim 1, wherein saidcompensating includes adding a term to the kurtosis-based sharpnessmetric based on a geometric mean (E_(x)*E_(y))^(1/2) of the averagehorizontal energy ({overscore (E_(x))}) and the average vertical energy({overscore (E_(y))}) and an arithmetic mean [½({overscore(E_(x))}+{overscore (E_(y))})] of the average horizontal energy({overscore (E_(x))}) and the average vertical energy ({overscore(E_(y))}) (16).
 8. The method according to claim 1, wherein saidcompensating includes adding a term to the kurtosis-based sharpnessmetric based on a number of blocks that contain edges (neb) (17).
 9. Themethod according to claim 1, wherein said compensating includes adding aterm to the kurtosis-based sharpness metric based on a number of blocksthat do not contain edges (nfb) (17).
 10. The method according to claim1, wherein said compensating includes adding a term to thekurtosis-based sharpness metric based on a number of blocks that containedges (neb) and a number of blocks that do not contain edges (nfb) (17).11. The method according to claim 4, wherein said compensating includesadding a term to the kurtosis-based sharpness metric based on an averagenumber of edge pixels per block ({overscore (nep)}) (14).
 12. The methodaccording to claim 7, wherein said compensating includes adding a termto the kurtosis-based sharpness metric based on an average number ofedge pixels per block ({overscore (nep)}) (14).
 13. The method accordingto claim 10, wherein said compensating includes adding a term to thekurtosis-based sharpness metric based on an average number of edgepixels per block ({overscore (nep)}) (14).
 14. The method according toclaim 12, wherein said compensating includes adding a term to thekurtosis-based sharpness metric based on an average horizontal energy({overscore (E_(x))}) and an average vertical energy ({overscore(E_(y))}) and an average diagonal energy ({overscore (E_(d))}) (15). 15.The method according to claim 11, wherein said compensating includesadding a term to the kurtosis-based sharpness metric based on a numberof blocks that contain edges (neb) and a number of blocks that do notcontain edges (nfb) (17).
 16. The method according to claim 4, whereinsaid compensating includes adding a term to the kurtosis-based sharpnessmetric based on a geometric mean (E_(x)*E_(y))^(1/2) of the averagehorizontal energy ({overscore (E_(x))}) and the average vertical energy({overscore (E_(y))}) and an arithmetic mean [½({overscore(E_(x))}+{overscore (E_(y))})] of the average horizontal energy({overscore (E_(x))}) and the average vertical energy ({overscore(E_(y))}). The ratio of the geometric to arithmetic mean raised to thepower of 2,$\frac{4*{\overset{\_}{E}}_{x}*{\overset{\_}{E}}_{y}}{\left( {{\overset{\_}{E}}_{x} + {\overset{\_}{E}}_{y}} \right)^{2}},$is the combined compensation term (16).
 17. The method according toclaim 16, wherein said compensating includes adding a term to thekurtosis-based sharpness metric based on a number of blocks that containedges (neb) and a number of blocks that do not contain edges (nfb) (17).18. The method according to claim 13, wherein said compensating includesadding a term to the kurtosis-based sharpness metric based on ageometric mean (E_(x)*E_(y))^(1/2) of the average horizontal energy({overscore (E_(x))}) and the average vertical energy ({overscore(E_(y))}) and an arithmetic mean [½({overscore (E_(x))}+{overscore(E_(y))})] of the average horizontal energy ({overscore (E_(x))}) andthe average vertical energy ({overscore (E_(y))}) (16).
 19. The methodaccording to claim 4, wherein said compensating includes adding a termto the kurtosis-based sharpness metric based on a number of blocks thatcontain edges (neb) and a number of blocks that do not contain edges(nfb) (17).
 20. The method according to claim 7, wherein saidcompensating includes adding a term to the kurtosis-based sharpnessmetric based on a number of blocks that contain edges (neb) and a numberof blocks that do not contain edges (nfb) (17).
 21. The method accordingto claim 14, wherein said compensating includes adding a term to thekurtosis-based sharpness metric based on a number of blocks that containedges (neb) and a number of blocks that do not contain edges (nfb) (17).22. The method according to claim 1, wherein the compensating includescalculating the following equation:${{Sh} = {{f_{1}\left\lbrack {C_{1} + {C_{2}*\overset{\_}{k}*\overset{\_}{nep}*\frac{\left( {{\overset{\_}{E}}_{x} + {\overset{\_}{E}}_{y} + {\overset{\_}{E}}_{d}} \right)}{{\overset{\_}{E}}_{d}}*\frac{4*{\overset{\_}{E}}_{x}*{\overset{\_}{E}}_{y}}{\left( {{\overset{\_}{E}}_{x} + {\overset{\_}{E}}_{y}} \right)^{2}}*\frac{neb}{nfb}}} \right\rbrack} + {C_{3}*\overset{\_}{nep}}}},$wherein: Sh is a sharpness metric; f₁ is a predetermined function; C₁,C₂ and C₃ are predetermined constants; {overscore (k)} is an averagekurtosis; {overscore (nep)} is an average number of edge pixels perblock; {overscore (E_(y))} is an average vertical energy; {overscore(E_(x))} is an average horizontal energy; {overscore (E_(d))} is anaverage diagonal energy; neb is a number of blocks that contain edges;and nfb is a number of blocks that do not contain edges (18).
 23. Themethod according to claim 7, wherein the average vertical and horizontalenergies are obtained by calculating values over the entire image (15).24. The method according to claim 7, wherein the average vertical andhorizontal energies are estimated from a sample of the image (15).
 25. Amethod for measuring sharpness in an image or picture comprising:performing a Discrete Cosine Transformation on each of a plurality ofblocks of a predetermined size of the image; and compensating forasymmetry using information on a number of edge pixels and an energycontent of one or more vertical edges and one or more horizontal edgesin each of the plurality of blocks (13).
 26. An image processingapparatus (40) comprising: an image detector (48 a-e) to convert theimage to an electronic version; and a sharpness controller (41) coupledto the image detector to detect sharpness in the electronic version ofthe image and adjust the sharpness, said controller to calculate asharpness metric of the image by: partitioning the image or picture intoone or more blocks, each of which has a predetermined size and repeatingthe following for each of the one or more blocks (11): determining akurtosis-based sharpness metric of the image (12); and compensating thekurtosis-based sharpness metric to account for differences in sharpnessenhancement in a horizontal direction and a vertical direction (13). 27.The apparatus according to claim 25, wherein said compensating includesadding a term to the kurtosis-based sharpness metric based on an averagenumber of edge pixels per block ({overscore (nep)}) (14).
 28. Theapparatus according to claim 25, wherein said compensating includesadding a term to the kurtosis-based sharpness metric based on an averagehorizontal energy ({overscore (E_(x))}) and an average vertical energy({overscore (E_(y))}) and an average diagonal energy ({overscore(E_(d))}) (15).
 29. The apparatus according to claim 25, wherein saidcompensating includes adding a term to the kurtosis-based sharpnessmetric based on a geometric mean (E_(x)*E_(y))^(1/2) of the averagehorizontal energy ({overscore (E_(x))}) and the average vertical energy({overscore (E_(y))}) and an arithmetic mean [½({overscore(E_(x))}+{overscore (E_(y))})] of the average horizontal energy({overscore (E_(x))}) and the average vertical energy ({overscore(E_(y))}) (16).
 30. The apparatus according to claim 25, wherein saidcompensating includes adding a term to the kurtosis-based sharpnessmetric based on a number of blocks that contain edges (neb) and a numberof blocks that do not contain edges (nfb) (17).
 31. The apparatusaccording to claim 28, wherein the average vertical and horizontalenergies are obtained by calculating values over the entire image (16).32. The apparatus according to claim 28, wherein the average verticaland horizontal energies are estimated from a sample of the image (16).